Modelled sonar and target depth distributions for active sonar operations in realistic environments

Andreassen, Kristoffer Engedal; Hjelmervik, Karl Thomas

doi:10.1121/2.0001610

Cited by 2 publications

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“…3 and 4 we can conclude that there is no single best depth when you take variability into account. Previous work 7 showed that this was already the case for a single environmental realisation due to mixed strategies, but even these mixed strategies are not themselves stable. In this work, however, we see that certain trends in the strategies, like the peak at 210 m in Fig.…”

Section: Discussionmentioning

confidence: 94%

“…4, remain stable throughout the varying environment. The peaks also display spread in depths, akin to the Gaussian filter applied to single mixed strategies previously 7 . The strategies aggregated over environmental variability are therefore more robust against slight variations than those generated from a single realisation.…”

Section: Discussionmentioning

confidence: 96%

“…The method proposed here expands upon previous work 7 by introducing spatially varying bottom depths and spatio-temporal variations in sound speed profiles (SSPs) and wind. To do this, we need to calculate a payoff matrix 10 which depends on the bearing θ and range r from the active sonar position and the current time t: U (z, d; t, θ, r) = (1 − U A (z, d; t, θ, r)) • U P (z; t, θ, r).…”

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confidence: 98%

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Variability in Modelled Sonar and Target Depth Distributions

ANDREASSEN,

HJELMERVIK

2024

International Conference on Underwater Acoustics 2024

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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 96%

mentioning

confidence: 98%

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Variability in Modelled Sonar and Target Depth Distributions

ANDREASSEN,

HJELMERVIK

2024

International Conference on Underwater Acoustics 2024

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“…Sonar system detection performance in the underwater environment relies strongly on the relative locations of the source, receiver, and target, as well as ambient conditions [25], [26]. In addition, acoustic wave propagation patterns and depth-dependent sound velocity profiles are important factors in finding the correct detection range and coverage underwater [27].…”

Section: Introductionmentioning

confidence: 99%

Three Dimensional Volume Coverage in Multistatic Sonar Sensor Networks

2022

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Recent changes in the design of enemy threats such as submarines and the technological achievements in sensor development have paved the way for multistatic sonar applications, which increase security and situational awareness in underwater tactical operations. Previously, coverage in multistatic sonar sensor networks (MSSN) was studied using Cassini ovals and the traditional sonar detection model in two dimensions without any discussion of the practicability and feasibility in terms of conditions related to the underwater acoustic propagation environment. In this study, a practical three-dimensional MSSN channel model is proposed. The proposed model covers a spectral variation of absorption loss, ambient noise, sound speed profile, and shadow zones. The realistic effects of sound propagation and environmental conditions are modeled and evaluated using Lybin, which is a well-known sonar performance prediction tool. Using the practical MSSN channel model, the number of source-receiver pairs required to cover a three-dimensional MSSN volume is calculated. The impacts of frequency, source-to-receiver distance, and source level are investigated. The results are compared to the Cassini oval and traditional sonar detection models via an error expression derived according to our verified practical model. The results reveal that the inclusion of ambient conditions and sound propagation characteristics in the channel model leads to huge error levels of 4700000% in the Cassini oval model and 170000% in the traditional sonar detection model, depending on frequency. Thus, the applicability of these models in realistic MSSN deployment scenarios is limited.

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Modelled sonar and target depth distributions for active sonar operations in realistic environments

Cited by 2 publications

References 0 publications

Variability in Modelled Sonar and Target Depth Distributions

Variability in Modelled Sonar and Target Depth Distributions

Three Dimensional Volume Coverage in Multistatic Sonar Sensor Networks

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